Prise en main de Data Science Workspace pour les spécialistes des données
Découvrez Data Science Workspace dans Adobe Experience Platform. Cette liste de lecture est conçue pour les spécialistes des données qui souhaitent apprendre à utiliser les notebooks JupyterLab pour obtenir des informations et des données de requête, créer des jeux de données activés pour le profil, publier des modèles d’apprentissage automatique automatisés et activer des insights appris par la machine pour les applications Adobe et non-Adobe.
Présentation de l’espace de travail de science des données
L’objectif de l’apprentissage automatique sur Adobe Experience Platform est de démocratiser la science des données en utilisant l’expertise du domaine des produits, clients et partenaires d’Adobe afin de créer un écosystème de services intelligents pour alimenter la prochaine génération d’expériences client. Data Science Workspace facilite l’accès aux données omnicanal, la création de modèles, la mise en oeuvre de modèles avec un déploiement en un clic et la consommation d’informations sur les modèles en les partageant via des profils client en temps réel. Cette vidéo donne un aperçu de Data Science Workspace et de la valeur qu’il offre aux entreprises.
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Présentation de l’architecture de Data Science Workspace
Cette vidéo décrit l’architecture globale et illustre les principaux composants de Data Science Workspace dans Adobe Experience Platform.
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Création du schéma de cours et du jeu de données
Découvrez comment créer le jeu de données et le schéma du cours Data Science Workspace utilisés dans le reste du cours.
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Cette vidéo explique comment créer un notebook JupyterLab et charger des données à partir de Adobe Experience Platform. Il indique également comment améliorer les performances de votre notebook lorsque vous utilisez de grandes quantités de données.
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Requête et découverte de données dans Data Science Workspace
Adobe Experience Platform vous permet d’utiliser le langage de requête structuré (SQL) dans l’espace de travail de science des données en intégrant Query Service à JupyterLab en tant que fonctionnalité standard.
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Analyse des données exploratoires dans Data Science Workspace
Le tutoriel sur l’analyse des données exploratoires (EDA) est conçu pour vous aider à découvrir des schémas dans les données, à vérifier la cohérence des données et à résumer les données pertinentes pour les modèles prédictifs.
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Présentation des recettes, des modèles et des services
Découvrez les recettes, les modèles et les services dans Adobe Experience Platform Data Science Workspace.
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Analyse des performances du modèle
Découvrez certaines des différentes méthodes utilisées pour analyser les performances d’un modèle, telles qu’une matrice de confusion, la précision, le rappel et la précision.
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Créez un modèle à l’aide du modèle de créateur de recettes.
Cette vidéo présente l’utilisation du modèle de créateur de recettes dans le lanceur de JupyterLab pour former et noter un modèle de propension et créer une recette.
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Création et publication d’un modèle formé
Découvrez comment créer, former, évaluer et publier un modèle à l’aide d’une recette créée avec le notebook du créateur de recettes JupyterLab.
184
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Planification de la formation et de la notation automatisées d’un service
Découvrez comment configurer la formation et la notation automatisées d’un service dans Data Science Workspace.
172
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Utilisation de la sortie d’apprentissage automatique dans la segmentation
Découvrez comment les sorties de modèle Workspace Data Science peuvent être utilisées dans Real-time Customer Profile et la segmentation.
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